Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

Kumar, Yogesh; Koul, Apeksha; Kaur, Sukhpreet; Hu, Yu‐Chen

doi:10.1007/s42979-022-01493-3

Cited by 17 publications

(3 citation statements)

References 46 publications

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“…The resulting values are then scaled to the desired range. Min-max normalization is well-suited for timeseries analysis as it addresses time-dependent data's specific scaling and range normalization requirements [49]. It preserves temporal relationships, handles seasonal and trend components, mitigates the influence of outliers, provides an interpretable scale, and is compatible with various modeling techniques.…”

Section: Data Preprocessingmentioning

confidence: 99%

Deep learning approaches for MIMO time-series analysis

Kurniawan,

Sulaiman,

Konate

et al. 2023

Int. J. Adv. Intell. Informatics

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This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior.

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Section: Data Preprocessingmentioning

confidence: 99%

Deep learning approaches for MIMO time-series analysis

Kurniawan,

Sulaiman,

Konate

et al. 2023

Int. J. Adv. Intell. Informatics

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“…Ballı S [ 25 ] indicated that the support vector machine method performed well in analyzing the temporal patterns of cumulative COVID-19 data. Kumar Y et al [ 26 ] predicted ten countries' pandemics by deep learning. Shetty RP et al [ 27 ] used an artificial neural network to predict COVID-19 cases in one state of India.…”

Section: Introductionmentioning

confidence: 99%

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

et al. 2023

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Background Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. Methods To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). Results A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (–0.81 and –0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (–0.03), Israel (–0.12), Italy (–0.02), and France (–0.03); all p < 0.05. Conclusions The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a “real-time” schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

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“…For the dynamic time series patterns of COVID-19, numerous studies have been conducted on modeling and forecasting since the outbreak began in 2019-2020. For instance, see [1][2][3][4][5][6][7][8][9][10] for remarkable works on the forecasting analysis of COVID-19. They dealt with ARIMA models and machine learning for COVID-19 pandemic forecasting.…”

Section: Introductionmentioning

confidence: 99%

Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models

Hwang

2023

Forecasting

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Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for improved prediction, characterizes and forecasts COVID-19 time series data marked by weekly oscillations. Parameter estimation and out-of-sample forecasting are carried out with data on daily COVID-19 infections and deaths between January 2021 and October 2022 in the USA, Germany, and Brazil, in which the COVID-19 data exhibit the strongest weekly cycle behaviors. Prediction accuracy measures, such as RMSE, MAE, and HMAE, are evaluated, and 95% prediction intervals are constructed. It was found that predictions of daily COVID-19 data can be improved considerably: a maximum of 55–65% in RMSE, 58–70% in MAE, and 46–60% in HMAE, compared to the existing models. This study provides a useful predictive model for the COVID-19 pandemic, and can help institutions manage their healthcare systems with more accurate statistical information.

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Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

Cited by 17 publications

References 46 publications

Deep learning approaches for MIMO time-series analysis

Deep learning approaches for MIMO time-series analysis

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models

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